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CLAUDE.md

Agent guidance for codellm-devkit/codeanalyzer-typescript (cants).

What this project is

cants is a TypeScript/JavaScript static analyzer built on the TypeScript compiler (via ts-morph). It is the CLDK TypeScript backend: it emits the canonical schema v2 — one additive Code Property Graph — in two projections, analysis.json and a Neo4j property graph. It mirrors its Python and Java sibling analyzers, so output-shape parity with them is a first-class concern.

Schema v2 — the additive CPG (read this before touching output)

The output is one scale-free structure: a containment tree of nodes (id / kind / span / children) with typed edge overlays (a CPG). Every classic artifact — symbol table, call graph, CFG, PDG, SDG — is a projection of that one structure, and analysis levels are how deeply it is populated (each level only ever adds, never rewrites):

  • L1 (-a 1): the tree to callable depth — application → symbol_table{module} → types{}/functions{}/fields{} → callables{} — plus call nodes in each callable's body{} (with callee unresolved). source is stored once per module; every node's text slices off it via span.bytes.
  • L2 (-a 2): the call_graph edge list (callable→callable) at the application scope, and the callee slot on each call node refined null → id (the one sanctioned mutation).
  • L3 (-a 3): the rest of body{} (statements + @entry/@exit) and the intra-callable edge lists cfg/cdg/ddg (reaching-definitions, prov:["reaching-defs"]) hung on each callable.
  • L4 (-a 4): the synthetic @formal_in:N/@formal_out/<L>/actual_in:N/<L>/actual_out vertices, the intra-caller summary edges, and the application-scope param_in/param_out lists (the interprocedural SDG).

Identity is two-tier: durable can://<lang>/<app>/<file>/<type>/<sig> ids at callable depth and above; ordinal <callable-id>@<line>:<col> (or @<tag>) below. Intra-callable edge lists use bare local ids; cross-callable lists use fully-qualified can://…@local ids. L1 ⊆ L2 ⊆ L3 ⊆ L4 is a CI-checkable monotonicity gate (test/schema-v2.test.ts). The model + every decision live in .claude/SCHEMA_DECISIONS.md (§ "Schema v2 migration") and the skillset's canonical-schema.md.

Provider/client boundary: the analyzer is a pure graph provider — it emits the graph substrate (CFG/PDG/SDG + summary edges) and stops. Slicing and taint are reachability queries over it and belong to the frontend SDK; never add a taint_flows section here.

The v1→v2 emitter is a pure transform in src/schema/v2/ (emit.ts reshapes the v1 in-memory model, dataflow.ts maps program_graphs into the tree) — the parse/resolve/ dataflow compute is untouched; only serialization is v2.

The call graph defaults to the union of two backends: the TS compiler's resolver and the embedded Jelly flow analyzer (which recovers higher-order/callback edges the resolver misses). Merged edges keep a provenance tag (tsc / jelly); --tsc-only or --call-graph-provider jelly selects one alone.

Architecture — follow the pipeline

The whole analyzer is one orchestration function: analyze() in src/core.ts. Read it first; everything else is a stage it calls, in order:

  1. materialize (src/build) — resolve/prepare the target project's deps.
  2. buildSymbolTable (src/syntactic_analysis) — modules, classes, interfaces, enums, type aliases, namespaces, functions, methods, variables, decorators, and JSDoc, with precise source spans.
  3. call graph (src/semantic_analysis) — selectProvider() picks tsc / jelly / union; each provider returns edges + external (phantom) symbols.
  4. program graphs (src/dataflow) — levels 3–4 (-a 3/-a 4): CFG → post-dominance/CDG → access-path def-use → PDG → SCC-condensed bottom-up summaries → SDG. This is the compute; it produces the internal program_graphs model, which src/schema/v2/dataflow.ts then maps into the v2 tree (body{} + cfg/cdg/ddg/summary per callable + param_in/ param_out). Decisions: .claude/SCHEMA_DECISIONS.md; contract + staged follow-ups: issue #2.
  5. cache (src/utils/cache.ts) — content-hash cache under .codeanalyzer/, so re-analysis only touches what changed (levels 3–4 also record summaries + dependency edges in graphs_summaries.json).
  6. output (src/schema/v2, src/build/neo4j) — src/schema/v2/emit.ts reshapes the v1 compute model into the schema-v2 analysis.json; src/build/neo4j projects the same v2 tree into a graph.cypher snapshot or an incremental Bolt push. --emit neo4j is always full-depth (levels gate the JSON path only; combining -a/--graphs with it is an error).

The output shape is schema v2 (src/schema/v2/model.ts, V2Application the top type); the types in src/schema (TSApplication) are the internal compute model the emitter transforms. The Neo4j schema (src/build/neo4j/schema.ts, v2.0.0) is versioned and enforced by a conformance test — treat both as contracts and keep them in lockstep with the JSON.

Directory map

Path Responsibility
src/main.ts, src/cli.ts Entry point + Commander CLI
src/core.ts analyze() orchestrator — the spine
src/options Parsed CLI options / AnalysisOptions
src/syntactic_analysis Symbol table (ts-morph traversal)
src/semantic_analysis Call-graph providers (tsc, jelly, union), phantoms
src/dataflow L3/L4 program-graph compute: CFG, dominance/CDG, def-use, summaries, SDG
src/schema TSApplication — the internal compute model + signatureOf + program_graphs
src/schema/v2 the schema-v2 emitter: model.ts (target shape) + emit.ts (tree/L1/L2) + dataflow.ts (L3/L4)
src/build Dep materialization; build/neo4j = the v2 graph projection (project/rows/cypher/bolt/schema)
src/utils fs, caching, logging, serialization (serialize.ts → toV2), version
test Bun tests + fixtures/sample-app + fixtures/dataflow-app; schema-v2.test.ts = the L1–L4 gates

Commands

  • bun run start -- --input /path/to/project — run the analyzer from source.
  • bun run build — compile the standalone dist/cants binary.
  • bun test — run tests. Container tests: bun run test:container (needs Docker).
  • bun run typecheck — tsc --noEmit.
  • bun run gen:schema — regenerate schema.neo4j.json.
  • bun run gen:readme — regenerate the README's cants --help block.

I implement features myself — you assist

For feature work, I write the implementation to stay fluent in my own analyzer. Act as a helper, not the author:

  • Don't write the feature code or apply edits to implement it unless I explicitly ask ("write this", "implement X", "apply it"). Default to guiding, not doing.
  • Do move me fast: explain the relevant stage, point at prior art (e.g. an existing call-graph provider in src/semantic_analysis as the template for a new one), sketch signatures/types, outline an approach, and answer questions about the codebase.
  • Review on request: when I share a diff or push, critique it — correctness, parity with the Python/Java backends, schema conformance, missing tests, edge cases — and suggest concrete improvements.
  • Scaffolding like tests or boilerplate is fine when I ask; otherwise leave the keyboard to me.
  • If you think I'm about to go wrong, say so briefly and let me decide — don't pre-empt by implementing the fix.

Rules

  1. Think before coding. State assumptions explicitly; ask rather than guess. Push back when a simpler approach exists. Stop when confused.
  2. Simplicity first. Guide me toward the minimum idiomatic code that solves the problem. Nothing speculative; no abstractions for single-use code.
  3. Issue → branch → work → PR. Every change starts as an issue, on a branch named feat/issue-XXX, fix/issue-XXX, chore/issue-XXX, and lands via a PR.
  4. Guard the contract. Changes to src/schema or Neo4j output must keep parity with the sibling analyzers and pass the schema conformance test.

Goal-driven execution, as a teaching loop

Success is measured by the sole fact that I understand it. The success criterion: I can point to the exact line of code where any feature lives, however remote or obscure, and explain why it's there and how it behaves.

To that end, be my teacher and a Socratic one — not an answer key:

  • Lead with questions that make me derive the answer; don't hand me the solution.
  • Verify understanding, not just behavior — have me locate and explain the relevant LOC, walk edge cases, and predict what a change would do before running it.
  • Teach, help improve, and strengthen the weak spots you surface; circle back to them.
  • The loop closes when I can teach it back and place every feature on a line, not merely when the tests pass.
  • Over the session, frequently — but not so much that I am stymied — ask spaced repetition questions so concepts are internalized.

Learning progress is tracked globally, not per-repo: see the SRS deck and the "continual learning" defaults in ~/.claude/CLAUDE.md.

Auxiliary support tasks

Tidy up the release announcement

Every vX.Y.Z tag makes the release workflow (.github/workflows/release.yml) auto-post an announcement to the Announcements discussion (this repo, and the org mirror in codellm-devkit/.github). That body is machine-generated from the release notes / PR titles — it mis-categorizes changes, includes chore(release) noise, and buries breaking changes.

When you're working in this repo, check whether the latest release's announcement still needs cleanup, and if so, fix it:

  1. Find it. gh release view --json tagName,publishedAt; then list recent discussions via gh api graphql (repository → discussions), match category Announcements and title vX.Y.Z. Keep the discussion node id and read its body.
  2. Skip if already done. If the body starts with <!-- cleaned-up --> (or already reads as a clear, human-written announcement), do nothing.
  3. Otherwise rewrite it into a clear, user-facing announcement, grounded in CHANGELOG.md and the referenced PRs/diff (not the auto-grouping — verify each change; never invent anything):
    • breaking changes first, each with a one-line migration step;
    • plain-language highlights (what it does, not the PR title);
    • upgrade lines — pip install -U "codeanalyzer-typescript==X.Y.Z", or brew upgrade codellm-devkit/homebrew-tap/codeanalyzer-typescript, or the shell installer one-liner;
    • links to the GitHub release and CHANGELOG.md.
  4. Update in place. Edit the discussion body with the GraphQL updateDiscussion mutation (don't open a new one), prepend <!-- cleaned-up -->, and mirror the same body to the org discussion. This task only reads code and edits Discussions — it makes no commits.